Adoption of a Machine Learning-Based Predictive Sampling Module in Retail Productive Credit Audits: A Modified UTAUT Model with Trust
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Abstract
Advances in Artificial Intelligence (AI) and machine learning have driven the use of data-driven systems in the audit process. One such implementation is the Machine Learning Predictive Model Information System (SIMA-MPML), which is used by Bank X’s Internal Audit department to support the selection of audit samples for retail productive loans. This study aims to analyze the factors influencing the acceptance and use of SIMA-MPML by auditors using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that incorporates the Trust variable. The study employs a quantitative approach using a survey method targeting auditors who use SIMA-MPML. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, and Trust have a positive and significant effect on Behavioral Intention. Additionally, Facilitating Conditions and Behavioral Intention have a positive and significant effect on Use Behavior. Social Influence is the variable with the greatest influence on Behavioral Intention. The results of the mediation test indicate that Behavioral Intention significantly mediates the effects of Performance Expectancy, Effort Expectancy, Social Influence, and Trust on Use Behavior. These findings suggest that the use of SIMA-MPML is influenced by perceived benefits, ease of use, workplace support, trust in the system, and organizational support. This study reinforces the relevance of the UTAUT model in explaining the adoption of machine learning-based technology in internal audit environments and highlights the importance of Trust as a factor supporting the acceptance of Artificial Intelligence-based technology.
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